基于改进YOLOv9的瓷砖表面缺陷检测
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1.华北理工大学人工智能学院 唐山 063210;2.上海电子信息职业技术学院机械与能源工程学院 上海 201411; 3.华北理工大学矿业工程学院 唐山 063210

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TQ174.76;TP391.4;TN911.73

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河北省省级科技计划项目(20327218D)、“唐山市卫生陶瓷质量智能监控技术基础创新团队”唐山市科技局项目(21130211D)、2023年上海电子信息职业技术学院高层次与紧缺人才科研启动经费项目(GCC2023006)资助


Detection of surface defects on ceramic tiles based on improved YOLOv9
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1.College of Artificial Intelligence, North China University of Science and Technology,Tangshan 063210, China;2.College of Mechanical and Energy Engineering, Shanghai Technical Institute of Electronics Information,Shanghai 201411, China; 3.College of Mining Engineering, North China University of Science and Technology,Tangshan 063210, China

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    摘要:

    针对瓷砖表面缺陷检测中存在的小目标检测精度低、参数量大,以及误检和漏检问题,提出了一种改进的瓷砖表面缺陷检测算法YOLOv9s-SEFN。首先,本研究设计了SPNet多尺度特征融合模块通过增强网络对多尺度特征的捕捉与融合能力,有效提升模型对瓷砖表面小缺陷检测的特征表达;其次,设计ECG轻量融合模块减少计算量和参数量以实现轻量化;然后,引入频率自适应扩张卷积(FADC)通过自适应调整膨胀率和频率选择,提升瓷砖小缺陷检测精度;最后,设计新的损失函数NWD-EIOU通过结合EIOU和NWD,提高小目标定位的精度。实验结果表明,与原YOLOv9s检测算法相比,改进后的YOLOv9s-SEFN算法在自建实验数据集上表现更佳,模型mAP@0.5提升至93.2%,提高了3.5%;召回率提升了4.96%;参数量减少了2.3%;浮点运算量降低了4.0%,能够满足瓷砖表面缺陷检测的需求。

    Abstract:

    Aiming at the problems of low accuracy of small target detection, large number of parameters, as well as misdetection and leakage in tile surface defect detection, an improved tile surface defect detection algorithm, YOLOv9s-SEFN, is proposed. Firstly, the SPNet multi-scale feature fusion module is designed in this study to effectively improve the model′s detection of small defects on the tile surface by enhancing the network′s capability of capturing and fusion of multi-scale feature expression; second, the ECG lightweight fusion module is designed to reduce the computational and parametric quantities to achieve lightweighting; then, the frequency adaptive dilation convolution (FADC) is introduced to improve the accuracy of small defects detection on tiles by adaptively adjusting the dilation rate and frequency selection; and lastly, a new loss function, NWD-EIOU, is designed to improve the accuracy of small target localization by combining EIOU and NWD. The experimental results show that compared with the original YOLOv9s detection algorithm, the improved YOLOv9s-SEFN algorithm performs better on the self-built experimental dataset, with the mAP@0.5 raised to 93.2%, an improvement of 3.5%; the recall rate is raised by 4.96%; the amount of parameters is reduced by 2.3%; and the amount of floating-point arithmetic is reduced by 4.0%, which is able to satisfy the needs of tile surface defect detection.

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尚梦佳,刘利平.基于改进YOLOv9的瓷砖表面缺陷检测[J].电子测量技术,2025,48(9):177-188

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  • 在线发布日期: 2025-05-23
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